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Find Similar Notes

find_similar_notes
Read-onlyIdempotent

Returns the K most semantically similar notes from your Obsidian vault's index given a source note path. Uses existing embeddings for fast, free discovery without live API calls.

Instructions

Given a note path, return the K most semantically similar notes from the index (excluding the source note). Uses the source note's existing chunk embeddings and anchors the source query to chunks aligned with the note's opening topic — no live API call to the embedding provider, so this is fast and free. Run index_vault first to populate embeddings for both the source and the candidates.

Input Schema

NameRequiredDescriptionDefault
pathYesVault-relative path to the source note, e.g. 'projects/atlas.md'.
limitNoMaximum number of similar notes to return (1-100, default: 10).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv4.1.1
    • removedInput schema / additionalProperties
      Removed value: -false
  2. Addedv2.0.0
  3. Removedv1.9.0
  4. Addedv1.8.0

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Adds real behavior beyond annotations: results exclude the source note, the tool reuses cached chunk embeddings rather than making a live API call, and is therefore 'fast and free.' That discloses cost/latency and an index-staleness dependency the annotations do not cover. It stops short of saying what happens when the index is missing or stale (error vs. empty result).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three front-loaded sentences with no filler; the core behavior comes first and the prerequisite last. The middle sentence about chunk embeddings and opening-topic anchoring is somewhat implementation-heavy but does explain why results are cheap and how they are ranked.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, yet the description never says what a result looks like — paths only, paths plus similarity scores, snippets? For a ranked-retrieval tool that omission is a meaningful gap, though the input side and behavioral context are otherwise well covered.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so both parameters are already documented in-schema with types, ranges, and defaults. The description maps 'note path' to path and 'K' to limit but adds no syntax, ranking, or scoring detail. Baseline 3 is appropriate when the schema carries the parameter burden.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource with scope: 'Given a note path, return the K most semantically similar notes from the index (excluding the source note).' The note-to-note input form distinguishes it operationally from query-based siblings like search_semantic and search_notes, and the exclusion rule removes ambiguity about the result set.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit prerequisite — 'Run index_vault first to populate embeddings for both the source and the candidates' — which tells the agent the ordering constraint. It does not explicitly name when to prefer search_semantic (query string) over this tool, leaving the alternative to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.